Corpus2graph is an open-source NLP-application-oriented tool that generates a word co-occurrence network from a large corpus.
Keyword extraction is used for summarizing the content of a document and supports efficient document retrieval, and is as such an indispensable part of modern text-based systems.
We present a fully unsupervised, extractive text summarization system that leverages a submodularity framework introduced by past research.
Combination of the proposed graph construction and scoring methods leads to a novel, parameterless keyword extraction method (sCAKE) based on semantic connectivity of words in the document.
This shows that the proposed method is independent of the domain, collection, and language of the training corpora.
In this paper, recent literature on automatic keyword extraction and text summarization are presented since text summarization process is highly depend on keyword extraction.